S100296 data-footprint-orlando-v1804a

IBM Systems Technical University © 2018 IBM Corporation
2018 IBM Systems
Technical University
Managing Risks with Data
Footprint Reduction
—
Tony Pearson
Master Inventor and Senior IT Architect,
IBM Corporation
April 30 – May 4, 2018
Orlando, Florida

Abstract
Managing a hybrid cloud is
not simple thing. It becomes
even harder when mixing
capacity techniques such as
thin provisioning and
reduction throughout your
hybrid storage solutions. The
management becomes much
more complex and you need
tools to manage it efficiently,
measure risks, and use policy
controls.
Come to hear how to do that
with IBM Hyper-Scale 5.0,
produced by IBM Spectrum
Accelerate team, the
software that drives XIV and
IBM FlashSystem A9000/R.
We will discuss the
advantages and risks of each
technique and how to control
and manage them.
2

This week with Tony Pearson
3
Day Time Topic
Monday 10:15am The 7 tiers of Business Continuity and Disaster Recovery
11:30am IBM Hybrid Cloud Storage Options
3:15pm
Reporting and Monitoring:
How to verify your storage is being used efficiently
Tuesday 11:30am
Introduction to IBM Cloud Object Storage System (powered by
Cleversafe)
Wednesday 3:15pm
Information Lifecycle Management: Why Archive is different
than Backup
Thursday 9:00am
Reporting and Monitoring:
How to verify your storage is being used efficiently
4:30pm Storage Meetup: Cloud and Object Storage BOF
Friday 9:00am Managing risks with Data Footprint Reduction
10:15am
The Pendulum Swings Back – Understanding Converged and
Hyperconverged Integrated Systems

Agenda
1. Introduction to data footprint
reduction technologies
2. How these technologies impact
Storage Management
3. IBM FlashSystem A9000 and
A9000R
4. The Hyper-Scale Manager GUI
4

Driving in Traffic to Work
31 minutes to work
(Plan)
Arrive 9:06
(Estimate)
5

Effective Distance
(estimate)
Fuel Remaining
(actual)
Do you have the Resources to Get There?
6

Terminology
= =
Physical “Raw” Capacity
• Actual media capacity
Usable Capacity
• Amount after RAID, Erasure Coding,
Spare capacity, metadata
Effective Capacity
• Amount estimated from benefits of
Compression, Thin Provisioning, and
Data Deduplication
Raw
Usable
Effective
Raw
Usable
Effective
JBOD
(Just a Bunch
of Drives)
Raw =
Usable =
Effective
Usable < Raw
RAID offers better
protection than JBOD
Effective > Usable
Data Footprint
Reduction stores more
data in same capacity
• 5:1 (5x)
• 80% savings
1.3x
5x
7

Fully Allocated vs. Thin Provisioning
Host sees fully
allocated amount
Actual data written
Allocated but unused space
dedicated to this host,
wasted space
Host sees full
virtual amount Actual data written
Empty space available to others
Physical Space Allocated
8

Lossy vs. Lossless Methods
Lossy
– Used with music, photos, video,
medical images, scanned
documents,
fax machines
Lossless
– Used with databases, emails,
spreadsheets, office documents,
source code
Good
enough?
Exactly
the same
Compress
Decompress
does not return
data back to its
original contents
Compress
Decompress
returns data
back to its
original contents
9

How Compression Works
Lempel-Ziv lossless compression builds a dictionary of
repeated phrases, sequences of two or more characters that
can be represented with fewer number of bits
In the above excerpt from “Lord of the Rings”, all of the red
text represents repeated sequences eligible for compression!
Source: The Lempel Ziv Algorithm, Christian Zeeh, 2003
10

Compressed Volumes based on Thin Provisioning
Actual data written
Allocated but unused space
dedicated to this host,
wasted until written to
Full
Actual data written
Physical Space
Allocated
Thin Provisioning
Host sees full
virtual amount
Physical Space
Allocated, up to 80%
reduction from actual
data written
Actual
data
written
Thin Provisioning
with Compression
11

FIVO vs. VIFO
Fixed Input, Variable Output
• WAN transmission
• Sequential tape
• IBM Spectrum Protect
• zip, tar, etc.
Variable Input, Fixed Output
• Random Access Compression
Engine™ (RACE)
• IBM SVC, Storwize V7000/F,
Storwize V5030/F
• FlashSystem V9000 and A9000/R
• XIV storage system Gen 3
1
2
3
4
5
6
Data
1
2
3
4
5
6
1
2
3
4
5
6
Compressed
Data
2
1
3
4
5
6
Data
Compressed
Data
12

Traditional Approaches
A
D
B
MN
G H
C
F
I
File
New
Compressed
File
ABC DMN FGH I
Blocks Shift
Compression after Modification
Real-time Compression
File
Compressed
File
A
D
B
MN
G H
C
F
I
File
New
Compressed
File
ABC D E F GHI MN
Compression after Modification
A
D
B
E
G H
C
F
I
ABC DEF GHI
The work to “update" a file may involve
many more I/Os
Data blocks shift
• Negative impact to deduplication
No notion of data location, data is
processed sequentially
The work to “update" a file about the
same or fewer I/O
Only modified block changed
• Deduplication friendly
Data location via map
Compression for Disk data
map
13

Data deduplication reduces capacity requirements by only
storing one unique instance of the data on disk and creating
pointers for duplicate data elements
1. Data elements are
evaluated to
determine a unique
signature for each
2. Signature values are
compared to identify
all duplicates
3. Duplicate data elements
are eliminated and
replaced with pointers to
reference element
Storage Optimization: Data Deduplication
14

Benefits Vary by Workload
Deduplication benefits can vary
greatly
Not all workloads gain from
deduplication !!!
Workloads that typically do gain:
– Backups
– Mail servers
– User files
– Virtualized environments (VM/VDI)
– Database copies
Databases typically do not benefit
from dedupe
Compression benefits vary as well
Data Source Reduction
Ratio
Compression
Savings
Databases,
Engineering Data
2- 5x 50-80%
Server/Desktop
Virtualization
1.7 - 4x 40-75%
Seismic Data 1.7 - 3x 40-70%
E-mail 1.4 - 5x 30-80%
15

Agenda
Storage Management
A9000R
16

Did You Sell More Tickets
than Seats available?
Over-Provisioning in the Airline Industry
17

Why Space is Over-Allocated
Scenario 1
Space requirements under-
estimated
Running out of space requires
larger volume
New request may take weeks to
accommodate
Application outage if not
addressed in time
Data must be moved to the
larger volume
Application outage during data
movement
Scenario 2
–Space requirements
over-estimated
–Capacity lasts for years
• No data migration
• No application outages
• No penalties
When faced with this
dilemma,
most will err on the side of
over-estimating
18

Data Footprint Reduction highlights for IBM FlashSystem
A9000/R
A B C D
A B C D
D C A B
A C B D
DEDUPLICATION COMPRESSION SNAPSHOTS
THIN
PROVISIONING
0100100
0000010
0001110
A B C D
A B C D
A B C D
•Supports scalable
workloads
•Global fingerprints DB
•Real time, primary
storage
•8K block size w/4K
alignment
• Redirect-on-
write
• Space-
efficient
• High-
performing
A B C D
• Hardware
accelerated
• Primary storage
• Real Time
• Enables high
utilization
efficiencies
Designed together for
comprehensive &
complementary reduction
PATTERN
REMOVAL
19

IBM FlashSystem A9000/R Data Reduction Process
Divide into 8K
extents
Generate
fingerprint
Match
existing
fingerprint?
Compress
unique data
Update
reference
count
+1
No
Yes
Fingerprint
•SHA1 hash code (160 bits / 20 bytes)
•Block length is 8 KB, aligned 4K I/O boundaries
Pattern
Removal
Incoming
data
Reduced
data
Match
standard
pattern?
Yes
No
Save Pattern
Identifier
ID
Meta
data
Look up existing
fingerprints
20

Data Footprint Reduction Methods for A9000R
• 8KB pattern detection
• Patterns -- static database of popular fingerprints
• More efficient than dedupe in both time and
space
• There are currently ~260 patterns
• Deduplication
• Compression
64KB
user write
Pattern
removal
Deduplication Compression
Data
Type
Dedupe Compress Combined
Virtual
Desktop
(VDI)
16.7x 2x 33x
KVM – Linux
guests 1.9x 3.8x 7.2x
Database
Restore +
Test
1.02x 4.2x 4.2x
21

Data Footprint Reduction- Accounting for Metadata
100GB
3.33GB
0.83GB
30x1
4x1
120x1
Ideal reduction
100GB
3.33GB
0.83GB
23x1
2.4x1
1GB
1GB+
+
=4.33GB
=1.83GB
100/4.33
4.33/1.83
54.6x1
Actual reduction
22

Estimating Potential Savings for Data Footprint Reduction
1) Evaluator
Group Estimator
Data reduction
estimations
based on tests
and experience.
No guarantees
Give a good
idea
Supports Linux, AIX, Windows, ESXi
CLI only
Requires root access
Has ability to run in batch mode
http://www.evaluatorgroup.com/
data-reduction-estimator/#
Available in Fix Central
2) IBM FlashSystem A9000/R
Data Reduction Estimation Tool
23

Agenda
Storage Management
A9000R
24

IBM FlashCore Technology: IBM MicroLatency™ Module
IBM Engineered
Massively Parallel Design
FPGAs in the Data Path
Distributed RAM
High Speed Interface
Hardware-based Data
at Rest Encryption,
Embedded Compression
12 Chips 28 Chips 56 Chips
3.6 TB usable 8.5 TB usable 18 TB usable
25

IBM FlashSystem – Variable Striped RAID
Chip 1
P1
P511
P342
P2
P172
P512
P171
Chip 2 Chip 3 Chip 16
Page–Based RAID
The pages across each chip
represent a RAID-5 group with
Interspersed Rotating Parity
3.6 TB 11+P
8.5 TB 13+P (2 sets)
18 TB 13+P (4 sets)
Page-based Rebuild
If a page fails, the data is
reconstructed from parity, and
written to the other pages on the
same set of chips.
Variable Striped RAID
The RAID group is then re-
defined without the failed page:
13+P 12+P 11+P etc.
P1
P511
P342
P2
P172
P512
P171
P1
P511
P342
P2
P172
P512
P171
P1
P511
P342
P2
P172
P512
P171
P341 P341P341 P341
26

IBM FlashCore Technology: Hardware Accelerated I/O
Engineered for Flash
Hardware RAID
Non-blocking Crossbar
Switch
Hardware Only Data Path
Single Box Highly Available
Architecture
Concurrent Code Load
Concurrent Maintenance
Canister-1 Canister-2
FC-1
MC-2MC-1
FC-3 FC-4FC-2
XBAR 0 XBAR 1
2-Dimensional RAID5 (10+P+S)
V
S
R
27

IBM MicroLatency Modules (12)
RAID Controllers (2)
Battery Packs (2)
Power Supplies (2)
Fan Packs (4)
Interface Modules (4)
Management Modules (2)
Canisters (2)
IBM FlashSystem 900 components
28
3D TLC with in-line compression
1.2 TB MLC * 3.6 TB 8.5 TB 18 TB
Usable 12 TB 36 TB 85 TB 180 TB
Effective 12 TB 110 TB 220 TB 220 TB
Performance 1.1 M IOPS 1.2 M IOPS
• FCP
• InfiniBand
• NVMe-OF
* Model 415

Components of FlashSystem A9000/A9000R
A9000
• 3 servers
• 1 FlashSystem 900
Grid Element
A9000R Grid Element
• 2 servers
• 1 FlashSystem 900
A9000R
• 2 to 6 Grid Elements
• 2 Infiniband switches
29

FlashSystem A9000/R Caching Methodology
Benefits
• Three copies in upper cache for
high availability
• Pattern Removal and Data
Deduplication
• Intel QuickAssist: Hardware-
assisted real-time compression
• Reduced data stored in lower cache
to increase hit ratios
• Reduction before Encryption to
optimize benefits of both
Upper cache
Lower cache
• Host Interface
• Sync Mirror, HyperSwap
• Compression
offloaded to Intel®
QuickAssist FPGA
• Async Mirroring
• Thin Provisioning
5x
effective
capacity!
• Pattern Removal
• Deduplication
• Encryption
• DistributionFlash
Enclosure
30

IBM FlashSystem A9000 and A9000R
* Based on estimated 5:1 reduction ratio
31
3.6 TB Entry 3.6 TB 8.5 TB 18 TB
Usable 22 TB 36 TB 85 TB 180 TB
Effective 110 TB 180 TB 425 TB 900 TB
Performance Up to 900K IOPS
3.6 TB 8.5 TB 18 TB
Usable 72-144 TB 170-340 TB 360-720 TB
Effective 360-720 TB 850-1700 TB 1.8 – 3.6 PB
Performance Up 1.2 to 2.4 M IOPS

Agenda
Storage Management
A9000R
32

Thin Provisioning and Data Footprint Reduction
Effective 1400 TB
(estimated)
1250 TB
Provisioned
700 TB
Data
written by
hosts
Critical 90% - 11/11/16
Major 80% - 10/10/16
Minor 70% - on 10/9/16
Warning 60% - on 3/6/16
600 TB
Usable
300 TB
Used
550 TB
Thin
Provision
savings
400 TB
Data
Footprint
Reduction
savings
What the
Host Sees
What the
Storage Admin Sees
What the
CIO Sees
33

Manage Capacity Efficiently
It’s time to manage
Capacity efficiently
Simpler allocation and
usage tracking using
smart widgets
34

Reclaimable Capacity
Reclaimable capacity
Manage your environment
efficiently
35

Capacity Growth Report
Capacity Growth Report
Forecast application usage
36

Capacity Analytics
Capacity analytics
Get capacity trend,
forecast and analysis
reports
37

Summary
Data footprint reduction
technologies magnify the effective
capacity of disk and flash devices
However, these technologies can
introduce risk if not managed
properly
IBM FlashSystem A9000 and
A9000R, and the Hyper-Scale
Manager GUI provide reports,
thresholds and analytics to help the
storage administrator
38

Please
complete
the session
survey!
39

SVC and Storwize 7.8.1 Quality Improvements
IBM is committed to delivering continual quality improvements in our products.
All SVC and Storwize platforms have traditionally delivered enterprise-levels of system availability,
however against this backdrop there is a constant drive to push quality beyond existing limits.
To this end, significant updates were introduced in the SVC and Storwize 7.8.1 release focusing
on increasing overall reliability.
7.8.1 was released in March 2017 and is currently running on over 35% of total systems in the
field. Field data shows that this release is consistently delivering over twice the system
availability and half the rate of node warmstarts compared with 7.7.1.
IBM is strongly recommending that all SVC and Storwize customers upgrade their systems to 7.8.1
(or a later release) at their next convenient opportunity. If you need help with this, please contact
IBM Systems Lab Services : ibmsls@us.ibm.com
41

About the Speaker
Tony Pearson is a Master Inventor and Senior IT Architect for the IBM Storage product line. Tony joined IBM Corporation in
1986 in Tucson, Arizona, USA, and has lived there ever since. In his current role, Tony presents briefings on storage topics
covering the entire IBM Storage product line, IBM Spectrum Storage software products, and topics related to Cloud Computing,
Analytics and Cognitive Solutions. He interacts with clients, speaks at conferences and events, and leads client workshops to
help clients with strategic planning for IBM’s integrated set of storage management software, hardware, and virtualization
solutions.
Tony writes the “Inside System Storage” blog, which is read by thousands of clients, IBM sales reps and IBM Business Partners
every week. This blog was rated one of the top 10 blogs for the IT storage industry by “Networking World” magazine, and #1
most read IBM blog on IBM’s developerWorks. The blog has been published in series of books, Inside System Storage: Volume
I through V.
Over the past years, Tony has worked in development, marketing and consulting for various storage hardware and software
products. Tony has a Bachelor of Science degree in Software Engineering, and a Master of Science degree in Electrical
Engineering, both from the University of Arizona. Tony is an inventor or co-inventor of 19 patents in the field of electronic data
storage.
9000 S. Rita Road
Bldg 9032 Floor 1
Tucson, AZ 85744
+1 520-799-4309 (Office)
tpearson@us.ibm.com
Tony Pearson
Master Inventor
Senior IT Architect
IBM Storage
42

IBM Tucson Client Experience Center
Tucson, Arizona is headquarters
for IBM storage hardware and
software design and development
IBM Tucson Client Experience
Center offers:
– Technology briefings
– Product demonstrations
– Solution workshops
– Lab tours
Take a video tour!
– http://youtu.be/CXrpoCZAazg
https://www.ibm.com/it-infrastructure/services/client-centers

Email:
tpearson@us.ibm.com
Twitter:
twitter.com/az990tony
Blog:
ibm.co/Pearson
Books:
www.lulu.com/spotlight/990_tony
IBM Expert Network on Slideshare:
www.slideshare.net/az990tony
Facebook:
www.facebook.com/tony.pearson.16121
LinkedIn:
https://www.linkedin.com/in/az990tony
Additional Resources from Tony Pearson

Notices and disclaimers
45
© 2018 International Business Machines Corporation. No part of this
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Information in these presentations (including information relating to
products that have not yet been announced by IBM) has been reviewed
for accuracy as of the date of initial publication and could include
unintentional technical or typographical errors. IBM shall have no
responsibility to update this information. This document is distributed
“as is” without any warranty, either express or implied. In no event,
shall IBM be liable for any damage arising from the use of this
information, including but not limited to, loss of data, business
interruption, loss of profit or loss of opportunity. IBM products and
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IBM products are manufactured from new parts or new and used parts.
In some cases, a product may not be new and may have been previously
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Workshops, sessions and associated materials may have been
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